| name | user-convo-summary |
| description | Summarize a Warp user's agent mode conversation history to understand how they use Warp. Use when asked to profile, summarize, or investigate what a specific user has been doing in agent mode (e.g., "summarize agent mode usage for user@example.com", "what has this user been using Warp for?", "get conversation info for this user").
|
User Conversation Summary
Summarize a user's agent mode history from their email address.
Schema config
The bq queries below use the default logical mapping: project YOUR_GCP_PROJECT,
core_user_facts (dataset prod) and agent_mode_messages_ugc (dataset
prod_ugc). Replace them with your physical names from
config/schema.json (see SCHEMA.md).
Step 1: Look Up user_id
bq query --use_legacy_sql=false --format=csv \
"SELECT user_id FROM \`YOUR_GCP_PROJECT.prod.core_user_facts\`
WHERE user_email = '[USER_EMAIL]' LIMIT 1"
Step 2: Fetch Recent Conversations
bq query --use_legacy_sql=false --format=csv \
"SELECT conversation_id, message_type, timestamp_utc, msg_content
FROM `YOUR_GCP_PROJECT.prod_ugc.agent_mode_messages_ugc`
WHERE user_id = '[USER_ID]'
ORDER BY timestamp_utc DESC
LIMIT 500"
Key message_type values:
user — the user's prompt (text at msg_content JSON path $.query)assistant — what the agent responded/did
Step 3: Analyze
- Extract
user messages (prompt via JSON_EXTRACT_SCALAR(msg_content, '$.query')) to understand what the user asked
- Extract
assistant messages to see what the agent did
- Group by
conversation_id to understand conversation flow
- Look for patterns in topics, projects, and workflows
Step 4: Write Summary
Write 1–2 paragraphs capturing:
- Primary use cases and topics
- Key tools/workflows leveraged
- Engagement patterns (conversation length, frequency, iterative vs. one-shot)
- Notable preferences or recurring patterns
